Semiparametric imputation-based inference for the additive mean residual life model with missing censoring indicators

Baolin Chen; Yong Zhou; Gregg E. Dinse

doi:10.4310/SII.241218235711

Semiparametric imputation-based inference for the additive mean residual life model with missing censoring indicators

Baolin Chen
, Yong Zhou^*
, Gregg E. Dinse

^*此作品的通讯作者

统计学院

Shanghai University of Finance and Economics
MOE Shanghai
Social & Scientific Systems Inc

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

This paper considers the additive mean residual life model for survival data with missing censoring indicators. Using nonparametric techniques, an imputation-based estimating equation method is developed. The asymptotic properties of the proposed estimators are established. Finally, the finite sample performance of the proposed estimation methods is evaluated via simulation studies and a real data application.

源语言	英语
页（从-至）	205-215
页数	11
期刊	Statistics and its Interface
卷	18
期	2
DOI	https://doi.org/10.4310/SII.241218235711
出版状态	已出版 - 2025

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10.4310/SII.241218235711

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@article{ceed38da968449029cc513ee84dad8f6,

title = "Semiparametric imputation-based inference for the additive mean residual life model with missing censoring indicators",

abstract = "This paper considers the additive mean residual life model for survival data with missing censoring indicators. Using nonparametric techniques, an imputation-based estimating equation method is developed. The asymptotic properties of the proposed estimators are established. Finally, the finite sample performance of the proposed estimation methods is evaluated via simulation studies and a real data application.",

keywords = "Additive mean residual life model, Imputation, Kernel smoother, Missing censoring indicator",

author = "Baolin Chen and Yong Zhou and Dinse, \{Gregg E.\}",

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AU - Zhou, Yong

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N2 - This paper considers the additive mean residual life model for survival data with missing censoring indicators. Using nonparametric techniques, an imputation-based estimating equation method is developed. The asymptotic properties of the proposed estimators are established. Finally, the finite sample performance of the proposed estimation methods is evaluated via simulation studies and a real data application.

AB - This paper considers the additive mean residual life model for survival data with missing censoring indicators. Using nonparametric techniques, an imputation-based estimating equation method is developed. The asymptotic properties of the proposed estimators are established. Finally, the finite sample performance of the proposed estimation methods is evaluated via simulation studies and a real data application.

KW - Additive mean residual life model

KW - Imputation

KW - Kernel smoother

KW - Missing censoring indicator

UR - https://www.scopus.com/pages/publications/85214090672

U2 - 10.4310/SII.241218235711

DO - 10.4310/SII.241218235711

M3 - 文章

AN - SCOPUS:85214090672

SN - 1938-7989

VL - 18

SP - 205

EP - 215

JO - Statistics and its Interface

JF - Statistics and its Interface

IS - 2

ER -

Semiparametric imputation-based inference for the additive mean residual life model with missing censoring indicators

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